Immigration documentation statuses evoke racialized faceism in mental representations

U.S. immigration discourse has spurred interest in characterizing who illegalized immigrants are or perceived to be. What are the associated visual representations of migrant illegality? Across two studies with undergraduate and online samples (N = 686), we used face-based reverse correlation and similarity sorting to capture and compare mental representations of illegalized immigrants, native-born U.S. citizens, and documented immigrants. Documentation statuses evoked racialized imagery. Immigrant representations were dark-skinned and perceived as non-white, while citizen representations were light-skinned, evaluated positively, and perceived as white. Legality further differentiated immigrant representations: documentation conjured trustworthy representations, illegality conjured threatening representations. Participants spontaneously sorted unlabeled faces by documentation status in a spatial arrangement task. Faces’ spatial similarity correlated with their similarity in pixel luminance and “American” ratings, confirming racialized distinctions. Representations of illegalized immigrants were uniquely racialized as dark-skinned un-American threats, reflecting how U.S. imperialism and colorism set conditions of possibility for existing representations of migrant illegalization.


Appendix Appendix A. Differences from pre-registration
The overall project centered on a descriptive examination of representations, so the preregistration was more general than what a confirmatory test would require.We list all deviations from the pre-registration in the followings: • We stated that we would have the faces rated on how "foreign" they were perceived.
After preregistering, we realized that this dimension could be unspecific in its referents (e.g., foreign from what vantage point and towards which direction?) and decided that "American" would be a more direct test of what we were interested in: inferences about their belongingness to the U.S. We did not collect ratings of "foreignness".• We stated that we would collect 120 total participants in the image generating task (40 in each documentation status condition).Our final sample instead contained 50 participants in each condition, totaling up to 150 participants.• We stated that we would exclude faces with an InfoVal of less than or equal to 1.56.An InfoVal is a numeric metric aimed at quantifying the informational value of a reverse correlation classification image (Brinkman et al., 2019).At the time of pre-registration, the InfoVal metric was yet to be published but reflected a novel and promising way to assess image reliability.However, while collecting data, we found that images that passed the 1.56 and more so the recommended 1.96 criterion were rare.We stopped data collection at 181 participants (N=59 citizen, N=70 documented, N=51 undocumented), and reassessed whether the exclusion criterion was too stringent.In order to ethically not discard a large amount of the data set that could be used, we decided to choose 50 faces from each condition from the data we had collected.These 50 faces would include those with the highest InfoVal scores, even if they were below criterion.So, the final sample included 150 total faces (50 from each condition), and through this procedure, 31 faces were excluded, those with the lowest InfoVal.Our difficulty obtaining high InfoValscoring faces reflect multiple possibilities.Maybe documentation statuses are not collectively solidified as visual stereotypes of gender categories are (gender was used to validate the InfoVal metric in Brinkman et. al. 2019).Another possibility is that our sample happened to collect unreliable data because of student characteristics or because our task was too difficult with 770 trials.In order to understand how our alternative exclusion procedure might have affected the resulting images, we created a classification image from only the faces that surpassed the InfoVal criterion in the undocumented immigrant condition (see image below).Both faces look nearly identical, suggesting that our average results in Study 1 would have been highly similar with the subset of only reliable images.This pattern suggests that the lower InfoVal-scoring images in our sample contribute little to the overall average.Lastly, this suggests that our individual classification face rating results in Study 1 are more conservative in that the results were obtained using individual face images that may have washed out any potential differences between conditions.
• We stated that the results of a power simulation with a hypothesized large effect size of f=.77 for the dangerous ratings that 20 participants would be enough for almost 100% power.Knowing that rating data from online samples is not always reliable, we stated that we would instead collect 40 participants per rating task in order to account for likely reliability exclusions.We achieved this sample size for the average classification image ratings which ended up exhibiting even larger effect sizes than .77.However, for the individual faces the sample sizes were often in the mid 30s, which is still above 20 participants, the minimum requirement estimated by our simulations.See figure below that tested how the number of participants affect power from the simulated model.
• We stated that we would collect 200 participants for the similarity sorting task, we instead collected 201 participants.The extra participant was a pilot participant to make sure the task worked.Since there were no issues, we decided to include them in the full sample.

Social and Economic
Note: Test-retest reliability was not calculated for the average face ratings as they were only rated once.
a,b Participants self-reported on a scale of 1 (never) to 7 (all the time) how often they are in contact with immigrants in general and with undocumented immigrants specifically on a daily basis.
c Participants responded whether they strongly oppose (1) or strongly favor (7) eight items related to preferences for group hierarchies (e.g., "Some groups of people are simply inferior to other groups.")(Ho et al., 2015).Item scores are reverse-corrected and averaged to compute a single score where higher numbers reflect greater dominance orientation.
d The Social and Economic Conservatism Scale (SECS) was used to assess political orientation (Everett, 2013).The scale presents 12 political topics (e.g., military, abortion, gun ownership, patriotism).Participants respond on a temperature-like scale (0 (negative) to 100 (positive)) how they feel about each issue.Scores are reverse-corrected and averaged across topics to produce a final score where closer to 0 is more liberal and closer to 100 is more conservative.a,b,c,d See Supplementary Table S1 for more information on the tagged individual difference measures.

Supplementary Table S2. Participant demographics in Study 1, individual face ratings
,b,c,dSee Supplementary TableS1for more information on the tagged individual difference measures. a